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Three instances of model-derived SST maps of Lake Ontario over the period of March-May, 2009. Plot (a) shows the SST map in mid March when cold water from the Niagara River discharge into warmer Lake Ontario. The middle map (b) indicates the temperature distribution in early April. The thermal bar formation is seen in early May (c) when warm waters enter the lake. The grid spacing for this simulation was 1.5 km with 3 m vertical resolution. The units are in degrees Celsius.
Source publication
Remote sensing has been proven as an effective tool for mapping and
monitoring water quality in coastal/inland waters during the past two
decades. In light of this, it can also be applied to calibrate
hydrodynamic models which predict the distribution of river plumes and
streams in coastal/inland waters. This research examines the capability
of Lan...
Contexts in source publication
Context 1
... is capable of performing a lake-wide simulation to derive the dynamics of the lake temperature and its circulations. Figure 1 illustrates three Lake Ontario SST maps from a two-month simulation, which spans early March to mid May, 2009. The evolution of the thermal bar is noticeable as simulation progresses into mid spring. ...
Citations
... Despite its limitations (mainly the spatial and temporal sensor resolutions trade-offs and the estimation is limited to the "skin temperature" -top 100 μm -only), it is a growing and well-established practice, since it provides information of spatiotemporal variation of water temperature ( Kay et al., 2005;Handcock et al., 2012;Dugdale, 2016). Although it has a more restrict range of applications in the literature, when compared to land surface (e.g., Fu and Weng, 2016) or lake surface water temperatures (LSWT) (e.g., Allan et al., 2016), it has being successfully applied in studies of spatiotemporal patterns (Díaz-Delgado et al., 2010;Ling et al., 2017) and longitudinal profiles of river temperatures ( Wawrzyniak et al., 2012;Fricke and Baschek, 2013), analysis of groundwater input to a river ( Lalot et al., 2015), and as inputs to a river temperature (Cristea and Burges, 2009) and hydrodynamic models (Pahlevan et al., 2011;Al-Murib et al., 2019;Munar et al., 2019). ...
Scarcity of water temperature data in rivers may limit a diversity of studies considering this property, which regulates many physical, chemical, and biological processes. We present a robust method to generate a consistent, continuous daily river water temperature (RWT) data series for medium and large rivers using the combined techniques of remote sensing and water temperature modelling. In order to validate our approach, we divided this study into two parts: (i) we evaluated methods to derive RWT from Landsat 7 ETM+ and Landsat 8 TIRS imagery; and (ii) we evaluated the calibration and validation of river temperature models, using these data, to generate the continuous RWT data series. A 1.2 km section of the White River located near Hazleton, IN, USA, was selected to assess this method mainly due to river width and data availability. We tested three methods to retrieve RWT from Landsat 7 and four from Landsat 8, and we also applied a simple thermal sharpening technique. For Landsat 7, the methods showed bias and RMSE of 0.01–0.46 °C and 1.32–1.84 °C, while for Landsat 8, the methods showed bias and RMSE of 0.08–1.27 °C and 1.74–2.17 °C, and in both cases, the best results were found applying the radiative transfer equation with NASA's Atmospheric Correction Parameter Calculator. For the second part of the validation process, we compared a stochastic model and a hybrid model, air2stream, using as input two datasets: the RWT data derived from Landsat 7 only, and a combined dataset of both Landsat 7 and 8 derived RWT. The air2stream model outperformed the stochastic model when calibrated with Landsat 7 data only, with RMSE of 1.83 °C, but both models showed similar results when calibrated with the combined Landsat data, when air2stream showed RMSE of 1.58 °C. Due to its physical basis, better calibration procedure, and higher consistency, air2stream was considered the best model for deriving the continuous RWT data series. When compared to the measured daily mean RWT data, there was no observed tendency in under or overestimating the RWT in low or high temperature conditions by the modelled series. While further tests are needed in order to evaluate if our approach can be applied to analyse past behaviour and present trends, and the impacts of climate change on the temperature of rivers, the consistent results indicate that this approach has the potential to be applied in rivers with no measured temperature data, for example, in the spatial modelling of longitudinal profiles of rivers and the modelling of tributary river temperatures.
... Ademais, representam as características da coluna de água em apenas um ponto do ecossistema em estudo. Para contornar a falta de dados e para uma melhor representatividade espacial, muitos autores têm recorrido ao uso integrado da modelagem matemática com dados de sensoriamento remoto (Pour et al., 2012;Pahlevan et al., 2011). ...
Lagos e reservatórios são ecossistemas extremamente importantes e, portanto, o monitoramento da qualidade de suas águas tem um papel fundamental na gestão de recursos hídricos. A temperatura da água, especialmente seu gradiente vertical, é uma variável determinante para a ocorrência de processos bioquímicos em tais ambientes aquáticos. Este trabalho busca a compreensão da hidrodinâmica de reservatório tropical, por meio da integração da modelagem matemática, do sensoriamento remoto e do monitoramento in situ, através do estudo de caso do reservatório Serra Azul, localizado em Minas Gerais, destinado ao abastecimento humano. Para isso, será utilizado o modelo hidrodinâmico unidimensional General Lake Model (GLM), bem como dados de sensoriamento remoto provenientes de imagens de Landsat, capturadas entre 1980 e 2002. Vinte e sete imagens de satélite foram selecionadas com base nas datas de monitoramento in situ e na nebulosidade. A temperatura da água superficial é obtida utilizando um algoritmo mono-canal. O modelo será manualmente calibrado e validado usando a temperatura da água medida in situ e a temperatura da água obtida a partir de imagens de satélite. Espera-se que os resultados nos permitam avaliar o uso do sensoriamento remoto para complementar o monitoramento do reservatório Serra Azul e entender melhor sua hidrodinâmica.
Remote sensing has traditionally been used to retrieve water constituents by establishing a relationship between in situ measured quantities and image-derived products. Motivated by the dramatically improved potential of the Landsat Data Continuity Mission (LDCM), this paper describes
a different approach for water constituent retrieval where both thermal and visible spectral bands of the Enhanced Thematic Mapper Plus (ETM [H11001]) instrument on board Landsat-7 are utilized. In this effort, Landsat data is integrated with a 3D hydrodynamic model to obtain profiles of particles
and dissolved matter in the near shore zone in the vicinity of two river discharges. The procedure is based upon performing many hydrodynamic simulations by adjusting input environmental/physical variables and generating Look-Up-Tables (LUTs). The best match, obtained using optimization, demonstrated
an average root-mean-squared-error (RMSE) of 0.68 percent, i.e., 0.0068 reflectance units, calculated over the two river plumes. It is concluded that calibrating a physics-based model using the Landsat-7 imagery can provide a more lucid insight into the dynamics of spatially non-uniform waters.
